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Neural Network Approach to Construct a Processing Map from a Non-linear Stress-Temperature Relationship

机译：神经网络方法构建非线性应力 - 温度关系的处理地图

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摘要

An accurate processing map for a metal provides a means of attaining a desired microstructure and required shape through thermo-mechanical processing. To construct such a map, the isothermal flow stress, sigma(iso), is required. Conventionally, the non-isothermal flow stress measured by experiment is corrected to sigma(iso) using whole-temperature-range linear interpolation (WRLI) or partial-temperature-range linear interpolation (PRLI). However, these approaches could incur significant errors if the non-isothermal flow stress exhibits a non-linear relationship with the temperature. In this study, an artificial neural network (ANN) model was applied to correct the non-isothermal flow stress in 10 wt% Cr steel, which exhibits a non-linear temperature dependence within a target temperature range of 750-1250 degrees C. Processing maps were constructed using sigma(iso) corrected by applying the WRLI, PRLI, and ANN approaches, respectively, and were then compared with the actual microstructures. The WRLI approach produced the highest minimum error of sigma(iso) (17.2%) and over-predicted the shear-band formation. The PRLI approach reasonably predicted the microstructural changes, but the minimum error for sigma(iso) (8.9%) was somewhat high. The ANN approach not only realized the lowest minimum error of sigma(iso) (similar to 0%), but also effectively predicted the microstructural changes.
机译：用于金属的精确处理图提供了通过热机械加工获得所需微观结构和所需形状的方法。为了构建这样的地图，需要等温流应力Sigma（ISO）。通常，使用实验测量的非等温流量应力使用全温范围线性插值（WRLI）或部分温度范围线性插值（PRLI）校正到Sigma（ISO）。然而，如果非等温流量应力表现出与温度的非线性关系，这些方法可能会产生重大误差。在该研究中，应用人工神经网络（ANN）模型以校正10wt％Cr钢中的非等温流量应力，其在750-1250℃的目标温度范围内表现出非线性温度依赖性。加工使用Sigma（ISO）通过应用WRLI，PRLI和ANN方法来构建地图，然后与实际的微观结构进行比较。 WRLI方法产生了SIGMA（ISO）的最高误差（17.2％）并过度预测了剪切带形成。 PRLI方法合理地预测了微观结构的变化，但Sigma（ISO）（8.9％）的最小误差有点高。 ANN方法不仅实现了Sigma（ISO）的最低误差（类似于0％），而且还有效地预测了微观结构的变化。

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